JSM 2024 Portland, Oregon
Aug 7, 2024
2020 Tokyo version
In Tokyo 2020, athletes are ranked from 1 to 8 (top - bottom) in each discipline. The final score is the multiplication of the ranks in each discipline.
| Athletes | Country | Speed | Boulder | Lead | Total | Rank |
|---|---|---|---|---|---|---|
| Janja Garnbret | Slovenia | 5 | 1 | 1 | 5 | 1 |
| Miho Nonaka | Japan | 3 | 3 | 5 | 45 | 2 |
| Akiyo Noguchi | Japan | 4 | 4 | 4 | 64 | 3 |
| Aleksandra Miroslaw | Poland | 1 | 8 | 8 | 64 | 4 |
| … | … | . | . | . | .. | .. |
Aleksandra Miroslaw gets 4th despite ranked last in both boulder and lead:
But she could win a medal if she performs better in the qualification round.
The game is on this week, but 1/4am PT 😭…
data with spatial (\(\mathbf{s}\)) and temporal (\(\mathbf{t}\)) dimensions: \[x_j(s;t)\]
The aggregate_*() function can be evaluated as a standalone recipe, before evaluated with the data in the dimension reduction module:
data %>% # data contain `prcp`
aggregate( # step 1: temporal aggregation
.var = prcp, # aggregate `prcp` with time scale
.scale = .scale) %>% # to create `.agg`, by default
dist_fit(.dist = .dist, # step 2: distribution fit
.method = "lmoms", # using L-moment to fit `.dist`
.var = .agg) %>% # distribution on `.agg`
augment(.var = .agg) # step 3: normalising
# find the normal density for `.agg`data %>% # data contain `tavg` and `prcp`
var_trans( # step 1: variable transformation
.method = "thornthwaite", # using the thornthwaite function
.vars = tavg, # on `tavg`
.new_name = "pet") %>% # to create a new variable `pet`
dim_red(diff = prcp - pet) %>% # step 2: dimension reduction
aggregate( # step 3: temporal aggregation
.var = diff, # aggregate `diff` with time scale
.scale = .scale) %>% # `.scale` to create `.agg`
dist_fit( # step 4: distribution fit
.dist = dist_gev(), # use the gev distribution
.var = .agg, # to fit the variable `.agg`
.method = "lmoms") %>% # using L-moment
augment(.var = .agg) # step 5: normalising
# find the normal density for `.agg`A bootstrap sample of 100 is taken from the aggregated precipitation series to estimate gamma parameters and to calculate the index SPI for the Texas Post Office station in Queensland.
DATA %>%
# aggregate monthly precipitation
# with a 24-month window
aggregate(
.var = prcp, .scale = 24
) %>%
# fit a gamma distribution to
# obtain the probability value
# [0, 1]
dist_fit(
.dist = gamma(), .var = .agg,
.n_boot = 100
) %>%
# use the inverse CDF to
# convert into z-score
augment(.var = .agg)tidyindex package:
https://sherryzhang-jsm2024.netlify.app